Seismic safety assessment of existing buildings is very important because their design and construction are made according to lower standards. The buildings designed with lower standards and without standards are susceptible to earthquake-induced damage. The vulnerability of existing buildings to seismic events has been vividly highlighted by recent earthquakes, such as the Türkiye–Syria earthquake on February 6, 2023, the Herat Afghanistan earthquake on October 11, 2023, and the Marrakesh-Safi Morocco earthquake on September 9, 2023. In the Turkey-Syria earthquake alone, over 50,000 people lost their lives [1], over 100,000 sustained injuries [2], and the economic toll amounted to approximately 110 million dollars [3]. Building damage from seismic events poses risks to lives and causes substantial financial losses, necessitating the determination of each building's fragility and the implementation of appropriate precautions before an impending devastating earthquake. Rapid Visual Screening (RVS) methods are employed for assessing building inventory, given the computational and cost constraints of in-depth vulnerability assessment methods. While conventional RVS methods are widely used and high efforts are given to enhance them, their reliability is limited for accurately assessing a building inventory [4–6]. Therefore, this study leverages post-earthquake building inspection data from the 2015 Gorkha, Nepal earthquake to develop a RVS method using artificial intelligence algorithms, encompassing fuzzy logic, machine learning, and neural networks. The integration of advanced feature engineering techniques introduces sophisticated parameters like fundamental structural period, spectral acceleration, and distance to the earthquake source, enhancing the RVS method's assessment capabilities across diverse seismically vulnerable areas. The developed RVS method demonstrates a correlation between observed building post-earthquake damage states and the predicted ones. When compared to conventional RVS methods, a noteworthy test accuracy of 44% is achieved, surpassing conventional methods in accurately classifying building damage states. Notably, in contrast to RVS methods solely developed using machine learning and neural networks, the developed method exhibits transparency and the capability to be adapted to different regions.

References: 

[1]        UN says at least 50,000 killed in Turkey and Syria quakes, AP News. (2023). https://apnews.com/article/turkey-syria-earthquakeunited-nations-44c2b736108ccb37130cf64e9e5fa7ca (accessed December 1, 2023).

[2]        Turkey and Syria earthquake: latest news, British Red Cross. (n.d.). https://www.redcross.org.uk/stories/disasters-and-emergencies/world/turkey-syria-earthquake (accessed December 1, 2023).

[3]        M. Ozturk, M.H. Arslan, H.H. Korkmaz, Effect on RC buildings of 6 February 2023 Turkey earthquake doublets and new doctrines for seismic design, Engineering Failure Analysis. 153 (2023) 107521. https://doi.org/10.1016/j.engfailanal.2023.107521.

[4]        N. Bektaş, F. Lilik, O. Kegyes-Brassai, Development of a fuzzy inference system based rapid visual screening method for seismic assessment of buildings presented on a case study of URM buildings, Sustainability. 14 (2022) 27.

[5]        N. Bektaş, O. Kegyes-Brassai, Development in Machine Learning Based Rapid Visual Screening Method for Masonry Buildings, in: M.P. Limongelli, P.F. Giordano, S. Quqa, C. Gentile, A. Cigada (Eds.), Experimental Vibration Analysis for Civil Engineering Structures, Springer Nature Switzerland, Cham, 2023: pp. 411–421. 

[6]        E. Harirchian, T. Lahmer, Developing a hierarchical type-2 fuzzy logic model to improve rapid evaluation of earthquake hazard safety of existing buildings, Applied Sciences (Switzerland). 10 (2020) 1384–1399.

How to cite: Bektaş, N.: Enhancing Seismic Safety Assessment Through Development of a Transparent and Adaptive Rapid Visual Screening Method Employing Artificial Intelligence Algorithms, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1012, https://doi.org/10.5194/egusphere-egu24-1012, 2024.

The Western Greece is one of the most tectonically active regions in the Mediterranean Sea, due to the subduction of the African plate underneath the Eurasian plate. The past years, major earthquakes occurred in Western Greece, causing destructions and casualties. Ancient Olympia, located in the North West of the Peloponnese in Western Greece, combines a great cultural background with natural beauty and is also associated with the Olympic Games. It is among the most visited archaeological sites in Greece, as it combines cultural tourism, eco-tourism and sports tourism. However, the complex tectonic field of Western Greece, including the broader area around Ancient Olympia, raises awareness and dictates the adoption of preventive and recovery measures in case of an earthquake risk in Western Peloponnese. Therefore, we propose an emergency and recovery plan for an earthquake risk scenario, that will be implemented in the broader area around the archaeological site of Ancient Olympia. Satellite and geospatial data are processed to extract all necessary thematic information. Additionally, multi-temporal InSAR analysis of Sentinel-1 images, is performed to identify areas exposed to ground deformation phenomena, therefore vulnerable during an earthquake. Detailed thematic information layers, combined with the identification of ground instabilities in the wider area around Ancient Olympia, will contribute to an efficient evacuation and reconstruction plan. Since cultural heritage sites are often exposed to various hazards, including geohazards, preparedness, risk assessment and emergency management near cultural heritage sites, is of great importance for their protection and preservation.

Acknowledgements

The research was funded by the Working Programme 2021 under the Caroline Herschel Framework Partnership Agreement on Copernicus User Uptake.

How to cite: Alatza, S., Stasinos, N., Stathopoulos, N., Papakonstantinou, M., Tsoutsos, M.-C., and Kontoes, C.: Geospatial and remote sensing analysis for earthquake risk management: The case study of Ancient Olympia archaeological site. , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18643, https://doi.org/10.5194/egusphere-egu24-18643, 2024.

How to cite: Ciracì, E., Frascella, C., Santarelli, F., Valerio, E., Scancella, S., and Chessa, A.: Mapping of Active Deformation Areas using Multi-Temporal InSAR data., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17388, https://doi.org/10.5194/egusphere-egu24-17388, 2024.

In the 20th century often the solution to mitigate floods was putting a corset of reinforced concrete to rivers. An example is one of the case studies in our project which we will detail, the Vidraru dam on Arges river, where a neighbourhood was built after the 1940 floods. In the 21st century however the approach is different. Several years ago a communication session was dedicated to achieving more flood resilience through floodplains on the Danube, and also several years ago a doctorate was concluded on landscape architecture solutions to mitigate floods on the Rhine. The geographic areas are different, and so are the localities and the early 20th century constructions which might be affected by floods and their relationship to the city - ex. peripheral Siedlung in Germany. Today one can look more systematically. A recent course of the European Commission offered insights to natural disaster mitigation through nature based solutions across the globe. We also reviewed literature in a nice part of this: decision systems to prioritise interventions based on cost-benefit to mitigate floods. On this basis, identifying gaps - the papers cover some of the aspects of this issue at once, not all of them - we elaborated a decision tree and its taxonomy according to criteria related to the building and the river landscape. The indices to quantify these criteria will be shown. This will be exemplified in case studies comprising Bucharest, Rome and Lisbon. It can be converted into an ontology for software planning, as in numerous European projects related to protection of architectural and archaeological heritage protected from climate change with IT support which go even further, towards Internet of Things. The context of future possible projects will be shown in the discussion.

How to cite: Bostenaru Dan, M.: Decision systems for nature based solutions to mitigate floods, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10438, https://doi.org/10.5194/egusphere-egu24-10438, 2024.

Floods are among the most frequent and damaging events worldwide, affecting population and residence buildings, economic activities, agriculture but also cultural heritage (hereinafter CH). Flood impacts to CH are very challenging to evaluate, due to their intangible values (e.g., spiritual, social, aesthetic) and difficulty in replacing unique objects. These aspects make the evaluation of CH exposure and vulnerability complex, also depending on the scale of analysis adopted.

This work aims at illustrating the differences in risk evaluations, as the scale of analysis varies. It highlights the information usually available for flood risk analysis of CH when moving from a large scale, e.g. regional/national level, to building scale, with different stakeholders’ perspectives, and demonstrates a very detailed approach for hazard modelling inside the CH building. The regional/national scale considers CH often as a point feature and usually aims at identifying geographic damage hotspots, i.e., river basin authority perspective. Few information is available at this scale, mostly hazard classification (low to high probability of occurrence) and often a tailored taxonomy for exposed assets has to be developed.

The site/city scale usually considers cultural heritage as a polygon feature with a better description of flood depths and of building characteristics such as the building type (e.g., religious or rural), the presence of underground floors or the presence of artworks. It identifies risk priorities and potential damage, i.e. it adopts a mayors’ perspective.

The building scale analysis, i.e., heritage manager perspective, requires moving towards a 3D geometric description of the cultural building by incorporating elevations of features with respect to the terrain to better understand actual inundation depths and their effects. On-site inspections are required to measure specific characteristics of the structure such as the location and the height of the openings, which let floodwater enter inside the building. Such information allows for a downscaling of a city inundation model, that provides the hydrograph to assign as boundary condition to the building.

The method is applied to the Florence area (central Italy) and to a museum inside the city center. The 2D inundation model at building scale shows the inundation inside the basement of the museum, formerly a crypt, where a part of the permanent art collection is exhibited. The museum model reveals the flow and the quantity of water within different part of the building. A comparison with the historical records of the 1966 flood in the Museum, confirms the findings of the simulation.

Acknowledgments. This work received co-funding by (i) Regione Toscana, Fondo per lo Sviluppo e la Coesione 2014-2020, Project “GiovaniSì” and by (ii) the RETURN Extended Partnership, European Union Next-GenerationEU (National Recovery and Resilience Plan – NRRP, Mission 4, Component 2, Investment 1.3 – D.D. 1243 2/8/2022, PE0000005)

How to cite: De Lucia, C., Castelli, F., and Arrighi, C.: Flood risk to cultural heritage: a voyage through scales, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11615, https://doi.org/10.5194/egusphere-egu24-11615, 2024.

Amidst global climate change, extreme weather events are becoming increasingly common. The international community has established 'preventive conservation' of cultural assets as a core strategy. Nations are actively devising measures to counter the potential impacts of climate change on cultural assets and developing related adaptive strategies. Particularly noteworthy is the often-overlooked potential risk of natural disasters to cultural assets, which poses a severe threat to these irreplaceable assets.

This study focuses on the Kinmen area of Taiwan and utilizes data provided by the Taiwan Climate Change Projection and Adaptation Knowledge Platform (TCCIP). Employing flood risk maps as the primary analytical tool, it delves into the changing risks faced by cultural assets under climate change and explores how to implement preventive conservation strategies.

The research reveals that during the baseline period (1980-2015), Kinmen had 287 cultural assets, with 4 facing the risk of flooding. According to the extreme weather scenario AR5 (RCP 8.5) provided by TCCIP, it is predicted that various regions will experience more intense rainfall conditions in the mid-21st century (2041-2065), increasing the risk of flooding and leading to more cultural assets (18 in total) being threatened. Therefore, through meteorological data projection and disaster risk assessment, this study advocates for early preventive conservation measures for high-risk cultural assets, aiming to mitigate the potential impact of climate change on these valuable assets.

How to cite: Chen, S.-C., Ma, K.-C., Chuang, M.-H., and Lin, W.-Y.: Changes in Flood Risk to Cultural Assets Under Climate Change - A Case Study of  Kinmen, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4495, https://doi.org/10.5194/egusphere-egu24-4495, 2024.

Being able to quantify the possible flood damage in a territory is a pivotal matter while designing strategies to mitigate risk. In this context, data driven quick assessment can be an useful tool to better understand the territory needs and to establish priorities.

With our work we have been working on the spatial distribution of perceived flood damage in Italy, analyzing data coming from more than 45000 citizens’ declarations registered after events occurred in Italy between 2013 and 2021. We focused on events which required the national state of emergency, which activated the procedure of claims' collection carried out by regions. Working on such material, we’ve been able to identify the subset of data which are specifically associated to flood events and to geolocalize them. Once the definitive dataset was ready we had a framework of the perceived damage at square meter in the country, which has been analyzed at different spatial scales. The work highlighted a great variability and inhomogeinity within different areas in the country. A further step of the analysis tried to link the perceived damage to the characteristic of the society and the territory, explaining what drives the perception of damage.

How to cite: Zambrini, F., Menduni, G., and D'Alessandro, S.: Analysis of the spatial distribution of perceived flood damage in Italy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19500, https://doi.org/10.5194/egusphere-egu24-19500, 2024.

Climate change and urbanization are expected to increase the risk of flood disasters in vulnerable areas. Urban road infrastructure can be affected by flooding, and subsequent restoration creates an additional carbon emission burden. These emissions are likely to compromise local decarbonization efforts, but there remains a lack of tools for quantifying the environmental impact of reconstruction projects after disasters. This study aims to develop an assessment framework to reveal the carbon footprint of post-flood road network restoration projects. The model integrates flood simulation, pavement damage evaluation, and carbon footprint calculation modules. This paper introduces nine flood scenarios ranging from 2-year to 1000-year events and a case study in Carlisle, UK, to test the integrated model. Results of the scenario simulation indicate that the carbon emissions from restoring per unit length of pavement as the flood magnitude increases for both main roads (10.46-19.21 kgCO₂e) and low-volume roads (5.34-10.17 kgCO₂e). Moreover, the case study indicates that the urban road network layout may significantly influence the general carbon footprint of post-flood pavement restoration. The carbon emissions from only restoring the main body of damaged pavements after this 70-hour disaster are estimated to offset almost 1% of the local decarbonization achievement for a month. Indirect carbon footprints from material production (67%) and delivery (29%) are much higher than direct emissions from on-site tasks (4%). Measures such as optimizing pavement materials, reusing wastes, rationalizing delivery routes, and improving the city layout help alleviate the burden of recovery. This study reflects on the environmental costs of disaster recovery processes, with a view to supporting improved mitigation strategies. The integrated modeling framework can also be applied to cities in different contexts to enrich reference for decision-making.

How to cite: Zhong, W., Howard, G., and Neal, J.: Estimating the carbon footprint of post-flood urban road network restoration: A case study in Carlisle, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-31, https://doi.org/10.5194/egusphere-egu24-31, 2024.

After the 2021 floods in Europe, independent data collection initiatives were undertaken in the impacted areas of Belgium and Germany. The resulting datasets at residential building level contain valuable information on hazard characteristics, vulnerability of exposed assets, socio-economic factors and coping capacity of the inhabitants and the emergency services (i.e., emergency and precautionary measures). A transnational analysis of these datasets enhances our understanding of flood damage mechanisms.

The data analysed resulted from 420, and 609 standardized surveys with private households affected by the 2021 floods in Belgium and Germany, respectively. Of these, 277 correspond to the area of Rhineland-Palatinate, and 332 were from North Rhine-Westphalia in Germany. A set of 64 potential damage influencing variables were harmonized across the datasets. The initial phase involved conducting descriptive statistics of the selected variables in three regions: the Vesdre valley in Belgium, the Ahr valley in Rhineland-Palatinate (Germany) and affected regions in North Rhine-Westphalia (Germany).

In a second step, the most influential variables for predicting flood damage to residential buildings were identified by means of feature selection. This was conducted using the linear approaches multilinear with k-best predictors, and Elastic net regression as well as the non-linear techniques Random Forest and Conditional Inference Trees. Total building loss and the total content loss were used as target values. Based on different evaluation metrics, the most important variables describing absolute building damage and absolute contents damage in the three analyzed areas, were identified.

Commonalities and differences in flood characteristics and damage in the three regions will be presented and interpreted in detail.

How to cite: Rodriguez Castro, D., Rafiezadeh Shahi, K., Sairam, N., Fischer, M., Samprogna Mohor, G., Thieken, A., Dewals, B., and Kreibich, H.: Machine-learning based feature selection for a regional flood damage model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15873, https://doi.org/10.5194/egusphere-egu24-15873, 2024.

This study focusing on the vulnerability and resilience in the downstream areas of the Da-an river basin (Dajia district and Houli district) centeral Taiwan. The study addresses the urban governance on compound disaster in the upstream Da-an river basin, including Taian township and Jhunan township in Miaoli county, Zhuolan township, and the Heping district in Taichung city. The research also explores how these townships adapt to extreme weather impacts and post-pandemic industrial adaptation. Our research employs various methods such as field surveys, in-depth interviews, literature reviews, and statistical data analysis: (1) By investigating and analyzing tourism camping areas in the aforementioned townships, we examine the environmental vulnerability of their industries and land use. This includes environmentally sensitive areas and agricultural and pastoral lands in indigenous areas, as well as issues related to vulnerable policies; (2) Agricultural production and marketing and government responses to different stages of impacts from extreme climates and diseases are being reviewed regarding the adaptive planning of local governments; (3) A vulnerability assessment framework applicable to settlements in islanding areas of the upper Da-an river is constructed by utilizing the Analytic Hierarchy Process (AHP) and Geographic Information System (GIS) for overlay analysis to discuss the relevant assessment influencing factors in camping areas. The research aims to clarify how watershed townships, under the influence of climate change, transform based on local tourism characteristics and agricultural industries post-pandemic. Particularly, it explores the mitigation measures demonstrated by local governments and private operators in the face of climate change and land exposure, showcasing the resilience of governance at different levels of government and private entities. Two aspects are found on this research: (1).The tourism vulnerability of townships in the upper Da-an river basin, particularly the crisis of camping tourism sites and land exposure, along with the island effect of their settlements. (2). Adaptive mechanisms of industrial resilience in watershed townships under the impact of the pandemic.

Keywords: river basin, extreme weather, islanding effect, city resilience, urban disaster, city vulnerability.

How to cite: Hung, C.-T., Lin, W.-Y., and Liu, C.-F.: The islanding effect of river basin city-regions under climate changes: vulnerability and resilience, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2761, https://doi.org/10.5194/egusphere-egu24-2761, 2024.

This study aims to explore the assessment of landslide hazards and adaptation faced by coastal communities under the impact of climate change. Considering the influence of climate change leading to increased frequency of extreme weather events, particularly heavy rainfall, which significantly affects the stability of hill slopes, this research will establish a shallow landslide susceptibility model for extreme rainfall events. This model can comprehensively solve the unsaturated transient Richard's equation and utilize the resultant pore water pressure along with the formula for the safety factor of unsaturated slope stability to construct a comprehensive regional landslide susceptibility analysis module. The module will generate distribution maps depicting the potential for slope collapses during extreme rainfall events. Through this analysis module, the study intends to predict collapse grids influenced by climate change and use Geographic Information Systems to create collapse susceptibility maps. We will overlay these maps with key infrastructure of coastal communities, including emergency response units, to understand the impact of landslide disasters on the infrastructure, emergency response capabilities during disasters, and post-disaster recovery capabilities of coastal communities. Furthermore, the study will explore the impact and adaptation strategies of climate change on infrastructure in coastal communities.

How to cite: Chuang, M.-H., Lin, S.-Y., Ma, K.-C., and Ku, C.-Y.: Study on the impact and adaptation strategies of climate change on infrastructure in coastal communities., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6859, https://doi.org/10.5194/egusphere-egu24-6859, 2024.

Recent years have seen a rise in both the intensity and frequency of storms, resulting in damage to coastal protection systems across the UK and globally. As a result, these systems have demanded substantial maintenance. For example, the gabion protection at Chesil Beach (Portland, UK) was severely damaged during the storms of February 2014, necessitating restoration costing at least £600 million. Similarly, the rock armor protection at Beesands (Devon, UK) also suffered damage in the same storm. These incidents highlight the urgent need to develop more resilient and innovative coastal defense systems. This fact gains further significance considering the UK's extensive coastal defense sector, necessitated by its vast coastlines with a length of approximately 30,000 km. According to various sources, approximately 18% of UK coastlines are protected with defense systems.

Here, we introduce an innovative coastal defense system comprising high-strength steel-wire mesh filled with rock. The system is securely fastened using tension rod ensuring its long-term integrity and stability. The diamond mesh in this system features units measuring 8.3 cm in width and 14.3 cm in height. These units are filled with uniformly-sized rock units, typically ranging in diameter from 15 cm to 25 cm. The high-tensile stainless-steel mesh, referred to as 'Tecco Cell’, is supplied by Geobrugg Inc. Therefore, we name this system as Tecco Cell (TC) revetment hereafter. The TC revetment carries the advantages of both gabion and rock revetments and minimizes their drawbacks. Gabion baskets or mattresses are susceptible to a significant weakness: their wire baskets can be damaged or broken by the force of strong waves. For rock armor, the rock units are displaced by strong waves resulting in the collapse of the defense system. The TC revetment systems alleviate both drawbacks; their high-strength mesh resists waves, while the tension rods, combined with the mesh, stabilize the system even against the strongest waves. The TC revetment was installed along a 120-meter stretch of the coast in Beesands (Devon, UK) in 2016. Over the past seven years, it has effectively defended the coast with minimal maintenance needs. Despite encountering several winter storms since its installation in 2016, the TC system in Beesands has remained resilient.

The purpose of this research is to report the results of two phases of laboratory tests on a 1/10 scaled model of a TC revetment. In Phase 1, eight tests were delivered on three types of revetments: gabion (two tests), rock armor (two tests) and TC revetments (four tests). In Phase 2, we conducted 32 tests comparing TC (16 tests) and rock armor (16 tests) revetments. It was found that the TC revetment consistently outperformed rock armor in terms of run up control and wave oscillations with an average runup reduction of 15%. We developed empirical equations for wave runup.

Thanks to the successful implementation of the TC revetment in Beesnads (UK), there is now consideration for applying this new coastal defense system in Ritoque (Chile) and along the northwestern coast of Italy.

How to cite: Heidarzadeh, M., Sheibani, M., and Luis-Fonseca, R. J.: A new revetment system based on high-strength steel-wire mesh filled with rock for coastal erosion control, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10972, https://doi.org/10.5194/egusphere-egu24-10972, 2024.

Disasters arising from geophysical hazards have the potential to trigger extensive structural damage upon the built environment within the impacted area. A substantial proportion of debris generated from earthquakes is a consequence of structural collapse during the ground motion, coupled with the urgent demolition of severely damaged and unstable structures in the course of emergency response and recovery. Among the foremost and pivotal measures undertaken during disaster management is the effective management of the generated debris. This task stands as one of the paramount challenges faced by those involved, given its inherent hazards to both the natural environment and public health. These hazards emanate from the presence of hazardous materials within debris from collapses and demolitions.

Numerous challenges and associated hazards emerged in southeastern Turkey after two devastating earthquakes on 6 February 2023 with Mw=7.8 and Mw=7.5 respectively. These seismic events affected a densely populated region encompassing 11 provinces, which included numerous sizable urban centers, such as large cities and towns, along with extensive rural areas comprising countless villages.

The convergence of intense ground motion, accompanied by the occurrence of widespread primary effects, such as coseismic surface ruptures, and the triggering of secondary effects, including mainly but not limited to liquefaction and landslides, culminated in the total or partial collapse of tens of thousands of structures and the extensive leveling of residential areas. This fact gave rise to a debris volume deemed the largest since the 1994 Northridge earthquake and challenging to manage, even within well-organized nations.

In the course of post-event field surveys conducted by the authors within the earthquake-stricken area, various disposal sites established in the most severely affected provinces were identified and assessed for suitability. The field surveys included the utilization of Unmanned Aircraft Systems (UAS) in the disaster-affected areas, complemented by the examination of satellite imagery in the laboratory to evaluate the characteristics of the sites and their immediate surroundings and to monitor the ongoing debris management activities.

The findings indicate that none of the identified sites possessed attributes qualifying them as safe for the treatment and disposal of earthquake debris. Primarily, this inadequacy is attributed to their close proximity to areas densely populated with thousands of residents who engage in daily activities. Furthermore, from the environmental viewpoint, these sites operated either within or in close proximity to surface water bodies. This situation reveals a rush for rapid debris removal and recovery resulting in serious omissions in the preparation of disaster management plans and concessions in their implementation. Consequently, recommendations for effective debris management measures are also proposed in the context of this research based on existing scientific knowledge and operational expertise.

How to cite: Mavrouli, M., Mavroulis, S., Vassilakis, E., Argyropoulos, I., Carydis, P., and Lekkas, E.: Debris Management in the Area Affected by the 6 February 2023 Turkey Earthquakes: Detecting Challenges, Hazards and Responses aiming to Effective Disaster Risk Reduction, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10164, https://doi.org/10.5194/egusphere-egu24-10164, 2024.